This repository contains the source for my paper, Understanding the Lomb-Scargle Periodogram. Abstract:
The Lomb-Scargle periodogram is a well-known algorithm for detecting and characterizing periodic signals in unevenly-sampled data. This paper presents a conceptual introduction to the Lomb-Scargle periodogram and important practical considerations for its use. Rather than a rigorous mathematical treatment, the goal of this paper is to build intuition about what assumptions are implicit in the use of the Lomb-Scargle periodogram and related estimators of periodicity, so as to motivate important practical considerations required in its proper application and interpretation.
The preprint available at https://arxiv.org/abs/1703.09824
All the code is contained in Jupyter notebooks in the figures subdirectory. Currently the organization of the code is somewhat haphazard; once the paper goes through review I'll attempt to organize the notebooks more clearly.
If you wish to cite this work, please use the bibliographic information available through NASA's Astrophysics Data System:
@ARTICLE{2017arXiv170309824V,
author = {{VanderPlas}, J.~T.},
title = "{Understanding the Lomb-Scargle Periodogram}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1703.09824},
primaryClass = "astro-ph.IM",
keywords = {Astrophysics - Instrumentation and Methods for Astrophysics},
year = 2017,
month = mar,
adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170309824V},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
The Code in the figures subdirectory is shared under the BSD-3-Clause license (read more at OSI). The text and figures are shared under the Creative Commons Attribution (CC-By) license (read more at CreativeCommons).